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50 LLM

Bhavishya Pandit

Interview Questions

Q1. What is tokenization, and why is it important in LLMs?

Ans - Tokenization is the process of splitting text into smaller units

called tokens, which can be words, subwords, or even characters. For

instance, the word “tokenization” might be broken down into smaller

subwords like “token” and “ization.” This step is crucial because LLMs

do not understand raw text directly. Instead, they process sequences

of numbers that represent these tokens.

Effective tokenization allows models to handle various languages,

manage rare words, and reduce the vocabulary size, which improves

both efficiency and performance.

Image Source: Cognitive Class

Bhavishya Pandit

Q2. What is LoRA and QLoRA?

Ans - LoRA and QLoRA are techniques designed to optimize the fine-

tuning of Large Language Models (LLMs), focusing on reducing

memory usage and enhancing efficiency without compromising

performance in Natural Language Processing (NLP) tasks.

LoRA (Low-Rank Adaptation)

LoRA is a parameter-efficient fine-tuning method that introduces

new trainable parameters to modify a model's behavior without

increasing its overall size. By doing so, LoRA maintains the original

parameter count, reducing the memory overhead typically associated

with training large models. It works by adding low-rank matrix

adaptations to the model's existing layers, allowing for significant

performance improvements while keeping resource consumption in

check.

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This makes it ideal for environments where computational resources

are limited, yet high model accuracy is still required.

QLoRA (Quantized LoRA)

QLoRA builds on LoRA by incorporating quantization to further

optimize memory usage. It uses techniques such as 4-bit Normal

Float, Double Quantization, and Paged Optimizers to compress the

model's parameters and improve computational efficiency. By

reducing the precision of model weights (e.g., from 16-bit to 4-bit)

while retaining most of the model's accuracy, QLoRA allows for the

fine-tuning of LLMs with minimal memory footprint. This method is

particularly useful when scaling large models, as it maintains

performance levels comparable to full-precision models while

significantly reducing resource consumption.

Bhavishya Pandit

Q3. What is beam search, and how does it differ from greedy

decoding?

Ans - Beam search is a search algorithm used during text generation

to find the most likely sequence of words. Instead of choosing the

single highest-probability word at each step (as greedy decoding

does), beam search explores multiple possible sequences in parallel,

maintaining a set of the top k candidates (beams). It balances

between finding high-probability sequences and exploring

alternative paths. This leads to more coherent and contextually

appropriate outputs, especially in long-form text generation tasks.

Bhavishya Pandit

Q4. Explain the concept of temperature in LLM text generation.

Ans - Temperature is a hyperparameter that controls the randomness

of text generation by adjusting the probability distribution over

possible next tokens. A low temperature (close to 0) makes the

model highly deterministic, favoring the most probable tokens.

Conversely, a high temperature (above 1) encourages more diversity

by flattening the distribution, allowing less probable tokens to be

selected. For instance, a temperature of 0.7 strikes a balance

between creativity and coherence, making it suitable for generating

diverse but sensible outputs.

Bhavishya Pandit

Q5. What is masked language modeling, and how does it contribute

to model pretraining?

Ans - Masked language modeling (MLM) is a training objective where

some tokens in the input are randomly masked, and the model is

tasked with predicting them based on context. This forces the model

to learn contextual relationships between words, enhancing its

ability to understand language semantics. MLM is commonly used in

models like BERT, which are pretrained using this objective to

develop a deep understanding of language before fine-tuning on

specific tasks.

Q6. What are Sequence-to-Sequence Models?

Ans - Sequence-to-Sequence (Seq2Seq) Models are a type of neural

network architecture designed to transform one sequence of data

into another sequence. These models are commonly used in tasks

where the input and output have variable lengths, such as in machine

translation, text summarization, and speech recognition.

Summarization

Chatbot

Language Translation

Cómo estás

A good teacher must

master these 3 qualities

to become successful

..................................1.

..................................2.

..................................3.

How are you?

I am good.

What about you?

3 qualities of an

excellent teacher

How are you?

Bhavishya Pandit

Q7. How do autoregressive models differ from masked models in

LLM training?

Ans - Autoregressive models, such as GPT, generate text one token at

a time, with each token predicted based on the previously generated

tokens. This sequential approach is ideal for tasks like text

generation. Masked models, like BERT, predict randomly masked

tokens within a sentence, leveraging both left and right context.

Autoregressive models excel in generative tasks, while masked

models are better suited for understanding and classification tasks.

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Q8. What role do embeddings play in LLMs, and how are they

initialized?

Ans - Embeddings are dense, continuous vector representations of

tokens, capturing semantic and syntactic information. They map

discrete tokens (words or subwords) into a high-dimensional space,

making them suitable for input into neural networks. Embeddings

are typically initialized randomly or with pretrained vectors like

Word2Vec or GloVe. During training, these embeddings are fine-

tuned to capture task-specific nuances, enhancing the model’s

performance on various language tasks.

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Q9. What is next sentence prediction and how is useful in language

modelling?

Ans - Next Sentence Prediction (NSP) is a key technique used in

language modeling, particularly in training large models like BERT

(Bidirectional Encoder Representations from Transformers). NSP

helps a model understand the relationship between two sentences,

which is important for tasks like question answering, dialogue

generation, and information retrieval.

During pre-training, the model is fed two sentences:

50% of the time, the second sentence is the actual next sentence in the document

(positive pairs).

50% of the time, the second sentence is a random sentence from the corpus

(negative pairs). The model is trained to classify whether the second sentence is

the correct next sentence or not. This binary classification task is used alongside a

masked language modeling task to improve the model's overall language

understanding.

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Q10. Explain the difference between top-k sampling and nucleus

(top-p) sampling in LLMs.

Ans - Top-k sampling restricts the model’s choices to the top k most

probable tokens at each step, introducing controlled randomness.

For example, setting k=10 means the model will only consider the 10

most likely tokens. Nucleus sampling, or top-p sampling, takes a

more dynamic approach by selecting tokens whose cumulative

probability exceeds a threshold p (e.g., 0.9). This allows for flexible

candidate sets based on context, promoting both diversity and

coherence in generated text.

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Q11. How does prompt engineering influence the output of LLMs?

Ans - Prompt engineering involves crafting input prompts to guide an

LLM’s output effectively. Since LLMs are highly sensitive to input

phrasing, a well-designed prompt can significantly influence the

quality and relevance of the response. For example, adding context

or specific instructions within the prompt can improve accuracy in

tasks like summarization or question-answering. Prompt engineering

is especially useful in zero-shot and few-shot learning scenarios,

where task-specific examples are minimal.

Q12. How can catastrophic forgetting be mitigated in large

language models (LLMs)?

Ans - Catastrophic forgetting happens when an LLM forgets

previously learned tasks while learning new ones, which limits its

versatility. To mitigate this, several strategies are used:

Rehearsal methods: These involve retraining the model on a mix

of old and new data, helping it retain knowledge of previous

tasks.

1.

Elastic Weight Consolidation (EWC): This method assigns

importance to certain model weights, protecting critical

knowledge while learning new tasks.

2.

Modular approaches: Techniques like progressive neural

networks (ProgNet) and optimized fixed expansion layers (OFELs)

introduce new modules for new tasks, allowing the LLM to learn

without overwriting prior knowledge.

3.

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Q13. What is model distillation, and how is it applied to LLMs?

Ans - Model distillation is a technique where a smaller, simpler

model (student) is trained to replicate the behavior of a larger, more

complex model (teacher). In the context of LLMs, the student model

learns from the teacher’s soft predictions rather than hard labels,

capturing nuanced knowledge. This approach reduces computational

requirements and memory usage while maintaining similar

performance, making it ideal for deploying LLMs on resource-

constrained devices.

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Q14. How do LLMs handle out-of-vocabulary (OOV) words?

Ans - Out-of-vocabulary words refer to words that the model did not

encounter during training. LLMs address this issue through subword

tokenization techniques like Byte-Pair Encoding (BPE) and

WordPiece. These methods break down OOV words into smaller,

known subword units. For example, the word “unhappiness” might be

tokenized as “un,” “happi,” and “ness.” This allows the model to

understand and generate words it has never seen before by

leveraging these subword components.

Q15. How does the Transformer architecture overcome the

challenges faced by traditional Sequence-to-Sequence models?

Ans - The Transformer architecture overcomes key limitations of

traditional Seq2Seq models in several ways:

Parallelization: Seq2Seq models process sequentially, slowing

training. Transformers use self-attention to process tokens in

parallel, speeding up both training and inference.

Long-Range Dependencies: Seq2Seq models struggle with long-

range dependencies. Transformers capture these effectively with

self-attention, allowing the model to focus on any part of the

sequence, regardless of distance.

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Positional Encoding: Since Transformers process the entire

sequence at once, positional encoding is used to ensure the

model understands token order.

Efficiency and Scalability: Seq2Seq models are slower to scale

due to sequential processing. Transformers, with their

parallelism, scale better for large datasets and long sequences.

Context Bottleneck: Seq2Seq uses a single context vector,

limiting information flow. Transformers let the decoder attend to

all encoder outputs, improving context retention.

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Q16. What is overfitting in machine learning, and how can it be

prevented?

Ans - Overfitting occurs when a machine learning model performs

well on the training data but poorly on unseen or test data. This

typically happens because the model has learned not only the

underlying patterns in the data but also the noise and outliers,

making it overly complex and tailored to the training set. As a result,

the model fails to generalize to new data.

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Techniques to Overcome Overfitting:

Regularization (L1, L2): Adding a penalty to the loss function to

discourage overly complex models. L1 (Lasso) can help in feature

selection, while L2 (Ridge) smooths weights.

Dropout: In neural networks, dropout randomly deactivates a

fraction of neurons during training, preventing the model from

becoming overly reliant on specific nodes.

Data Augmentation: Expanding the training dataset with slight

variations, such as flipping or rotating images, to make the

model more robust.

Early Stopping: Monitoring the performance of the model on

validation data and stopping training when the validation loss

stops decreasing.

Simpler Models: Reducing the complexity of the model by

decreasing the number of features, parameters, or layers can

help avoid overfitting.

Bhavishya Pandit

Q17. What are Generative and Discriminative models?

Ans - In NLP, generative and discriminative models are two key types

of models used for various tasks.

Generative models learn the underlying data distribution and

generate new samples from it. They model the joint probability

distribution of inputs and outputs, aiming to maximize the

likelihood of the observed data. A common example is a language

model, which predicts the next word in a sequence based on previous

words.

Discriminative models focus on learning a decision boundary

between different classes in the input-output space. They model the

conditional probability of outputs given inputs, aiming to accurately

classify new examples. An example is a sentiment analysis model,

which classifies text as positive, negative, or neutral based on its

content.

In short, generative models generate data, while discriminative

models classify it.

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Q18. How is GPT-4 different from its predecessors like GPT-3 in

terms of capabilities and applications?

Ans - GPT-4 introduces several advancements over its predecessor,

GPT-3, in terms of both capabilities and applications:

Improved Understanding: GPT-4 has roughly 1 trillion

parameters, significantly more than GPT-3’s 175 billion

parameters.

Multimodal Capabilities: GPT-4 processes both text and images,

a major leap over GPT-3, which is text-only.

Larger Context Window: GPT-4 can handle inputs with a much

larger context window of up to 25,000 tokens, while GPT-3 maxes

out at 4,096 tokens.

Better Accuracy and Fine-Tuning: GPT-4 has been fine-tuned to

be more factually accurate, reducing the likelihood of producing

false or harmful information.

Language Support: GPT-4 has improved multilingual

performance, supporting up to 26 languages with higher accuracy

compared to GPT-3’s performance in non-English languages.

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Q19. What are positional encodings in the context of large language

models?

Ans - Positional encodings are essential in Large Language Models

(LLMs) to address the inability of transformer architectures to

capture sequence order. Since transformers process tokens

simultaneously through self-attention, they are unaware of token

order. Positional encodings provide the necessary information to

help the model understand the sequence of words.

Mechanism:

Additive Approach: Positional encodings are added to input word

embeddings, merging static word representations with positional

data.

Sinusoidal Function: Many LLMs, such as the GPT series, use

trigonometric functions to generate these positional encodings.

Formula:

Where:

pos is the position in the sequence

i is the dimension index (0 ≤ i < d_model/2)

d_model is the dimensionality of the model

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Q20. What is Multi-head attention?

Ans - Multi-head attention is an enhancement of single-head

attention, allowing a model to attend to information from different

representation subspaces simultaneously, focusing on various

positions in the data. Instead of using a single attention mechanism,

multi-head attention projects the queries, keys, and values into

multiple subspaces (denoted as h times) through distinct learned

linear transformations.

This process involves applying the attention function in parallel to

each of these projected versions of the queries, keys, and values,

which generates multiple output vectors. These outputs are then

combined to produce the final dv-dimensional result. This approach

improves the model's ability to capture more complex patterns and

relationships in the data.

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Q21. Derive the softmax function and explain its role in attention

mechanisms.

Ans - The softmax function transforms a vector of real numbers into a

probability distribution. For an input vector x = [x_1, x_2, ..., x_n] the

softmax function for the i-th element is defined as:

This ensures all output values lie between 0 and 1 and sum to 1,

making them interpretable as probabilities.

In attention mechanisms, softmax is applied to the attention scores

to normalize them, allowing the model to assign varying levels of

importance to different tokens when generating output. This helps

the model focus on the most relevant parts of the input sequence.

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Q22. How is the dot product used in self-attention, and what are its

implications for computational efficiency?

Ans - In self-attention, the dot product is used to calculate the

similarity between query (Q) and key (K) vectors. The attention

scores are computed as:

Where d_k is the dimensionality of the key vectors. The dot product

measures alignment between tokens, helping the model decide

which tokens to focus on. While effective, the quadratic complexity

(O(n^2)) in sequence length can be a challenge for long sequences,

prompting the development of more efficient approximations.

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Q23. Explain cross-entropy loss and why it is commonly used in

language modeling.

Ans - Cross-entropy loss measures the difference between the

predicted probability distribution and the true distribution (one-hot

encoding of the correct token). It is defined as:

Where is the true label and is the predicted probability. Cross-

entropy loss penalizes incorrect predictions more heavily,

encouraging the model to output probabilities that are closer to 1 for

the correct class. In language modeling, it ensures the model

predicts the correct token in a sequence with high confidence.

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Q24. How do you compute the gradient of the loss function with

respect to embeddings?

Ans - To compute the gradient of the loss L with respect to an

embedding vector E, you apply the chain rule:

Here, is the gradient of the loss with respect to the output logits,

and is the gradient of the logits with respect to the embeddings.

Backpropagation propagates these gradients through the network

layers, adjusting the embedding vectors to minimize the loss.

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Q25. What is the role of the Jacobian matrix in backpropagation

through a transformer model?

Ans - The Jacobian matrix represents the partial derivatives of a

vector-valued function with respect to its inputs. In backpropagation,

it captures how each element of the output vector changes with

respect to each input. For transformer models, the Jacobian is

essential in computing gradients for multi-dimensional outputs,

ensuring that each parameter (including weights and embeddings) is

updated correctly to minimize the loss function.

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Q26.Explain the concept of eigenvalues and eigenvectors in the

context of matrix factorization for dimensionality reduction.

Ans - Eigenvalues and eigenvectors are fundamental in

understanding the structure of matrices. Given a matrix A, an

eigenvector and eigenvalue satisfy the equation:

In dimensionality reduction techniques like PCA (Principal

Component Analysis), eigenvectors represent the principal

components, and eigenvalues indicate the amount of variance

captured by each component. Selecting components with the largest

eigenvalues helps reduce dimensionality while preserving most of

the data's variance.

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Q27. How is the KL divergence used in evaluating LLM outputs?

Ans - KL (Kullback-Leibler) divergence measures the difference

between two probability distributions P (true distribution) and Q

(predicted distribution):

In LLMs, it quantifies how much the predicted distribution deviates

from the target distribution. A lower KL divergence indicates that the

model’s predictions closely match the true labels, making it a useful

metric in evaluating and fine-tuning language models.

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Q28. Derive the formula for the derivative of the ReLU activation

function and discuss its significance.

Ans - The ReLU (Rectified Linear Unit) function is defined as:

Its derivative is:

ReLU introduces non-linearity while maintaining computational

efficiency. Its sparsity (outputting zero for negative inputs) helps

mitigate the vanishing gradient problem, making it a popular choice

in deep learning models, including LLMs.

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Q29. What is the chain rule in calculus, and how does it apply to

gradient descent in deep learning?

Ans - The chain rule states that the derivative of a composite

function is:

In deep learning, the chain rule is used in backpropagation to

compute gradients of the loss function with respect to each

parameter layer by layer. This allows gradient descent to update

weights efficiently, propagating error signals backward through the

network.

Bhavishya Pandit

Q30. How do you compute the attention scores in a transformer, and

what is their mathematical interpretation?

Ans - Attention scores in a transformer are computed as:

Here, Q (queries), K (keys), and V (values) are learned

representations of the input. The dot product measures the

similarity between queries and keys. Scaling by prevents

excessively large values, ensuring stable gradients. The softmax

function normalizes these scores, emphasizing the most relevant

tokens for each query, guiding the model’s focus during generation.

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Q31. In what ways does Gemini’s architecture optimize training

efficiency and stability compared to other multimodal LLMs like

GPT-4?

-Gemini’s architecture optimizes training efficiency and stability

compared to multimodal models like GPT-4 in several ways:

1.Unified Multimodal Design: Gemini integrates text and image

processing in a single model, improving parameter sharing and

reducing complexity.

2.Cross-Modality Attention: Enhanced interactions between text and

images lead to better learning and stability during training.

3.Data-Efficient Pretraining: Self-supervised and contrastive

learning allow Gemini to train with less labeled data, boosting

efficiency.

4.Balanced Objectives: Better synchronization of text and image

losses ensures stable training and smoother convergence.

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Q32. What are different types of Foundation Models?

-Foundation models are large-scale AI models trained on vast

amounts of unlabeled data using unsupervised methods. They are

designed to learn general-purpose knowledge that can be applied to

various tasks across domains. Common Types of Foundation Models-

1.Language Models -

Tasks: Machine translation, text summarization, question answering

Examples: BERT, GPT-3

2.Computer Vision Models -

Tasks: Image classification, object detection, image segmentation

Examples: ResNet, VGGNet

3.Generative Models -

Tasks: Creative writing, image generation, music composition

Examples: DALL-E, Imagen

4.Multimodal Models -

Tasks: Image captioning, visual question answering

Examples: PaLM, LaMDA

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Q33. How does Parameter-Efficient Fine-Tuning (PEFT) prevent

catastrophic forgetting in LLMs?

-

Parameter-Efficient Fine-Tuning (PEFT) helps prevent catastrophic

forgetting in LLMs by updating only a small set of task-specific

parameters, while keeping most of the model's original parameters

frozen. This approach allows the model to adapt to new tasks without

overwriting previously learned knowledge, ensuring it retains core

capabilities while learning new information efficiently.

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Q34. What are the key steps involved in the Retrieval-Augmented

Generation (RAG) pipeline?

Key steps in the Retrieval-Augmented Generation (RAG) pipeline are:

1.Retrieval: The query is encoded and compared with precomputed

document embeddings to retrieve relevant documents.

2. Ranking: The retrieved documents are ranked based on their

relevance to the query.

3. Generation: The top-ranked documents are used as context by the

LLM to generate more informed and accurate responses.

This hybrid approach enhances the model’s ability to produce

context-aware outputs by incorporating external knowledge during

generation.

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Q35. How does the Mixture of Experts (MoE) technique improve LLM

scalability?

Mixture of Experts (MoE) improves LLM scalability by using a gating

function to activate only a subset of expert models (sub-networks)

for each input, rather than the entire model.

This selective activation:

Reduces computational load: Only a few experts are active per

query, minimizing resource usage.

Maintains high performance: The model dynamically selects the

most relevant experts for each input, ensuring task complexity is

handled effectively.

MoE enables efficient scaling of LLMs, allowing larger models with

billions of parameters while controlling computational costs.

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Q36. What is Chain-of-Thought (CoT) prompting, and how does it

improve complex reasoning in LLMs?

-Chain-of-Thought (CoT) prompting helps LLMs handle complex

reasoning by encouraging them to break down tasks into smaller,

sequential steps. This improves their performance by:

Simulating human-like reasoning: CoT prompts the model to

approach problems step-by-step, similar to how humans solve

complex issues.

Enhancing multi-step task performance: It's particularly effective

for tasks involving logical reasoning or multi-step calculations.

Increasing accuracy: By guiding the model through a structured

thought process, CoT reduces errors and improves performance

on intricate queries.

CoT improves LLMs' interpretability and reliability in tasks that

require deeper reasoning and decision-making.

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Q37. What is the difference between discriminative AI and

Generative AI?

-Predictive/Discriminative AI:

Focuses on predicting or classifying data based on existing data.

It models the conditional probability P(y|x)P(y|x)P(y|x), where y

is the target variable and x represents the input features.

Examples include tasks like classification (e.g., image

recognition), regression (e.g., predicting stock prices), and

applications such as spam detection and disease diagnosis.

Generative AI:

Focuses on generating new data samples that resemble the

training data. It models the joint probability P(x,y)P(x, y)P(x,y),

allowing it to create new instances of data

Examples include generating text, images, music, and other

content. Techniques used are Generative Adversarial Networks

(GANs), Variational Autoencoders (VAEs), and large language

models like GPT.

Bhavishya Pandit

Q38. How does knowledge graph integration enhance LLMs?

Integrating knowledge graphs with LLMs enhances performance by

adding structured, factual knowledge. Key benefits include:

Factual accuracy: The model can cross-check information against

the knowledge graph, reducing hallucinations and improving

correctness

Enhanced reasoning: Knowledge graphs support logical

reasoning by leveraging relationships between entities, enabling

better handling of complex queries.

Contextual understanding: Structured data helps the model

understand context and relationships, improving response

quality.

This integration is particularly valuable in tasks like question

answering, entity recognition, and recommendation systems, where

structured knowledge plays a critical role.

Bhavishya Pandit

Q39. What is zero-shot learning, and how does it apply to LLMs?

Zero-shot learning enables LLMs to perform tasks they haven't been

explicitly trained for by leveraging their broad understanding of

language and general concepts. Instead of needing task-specific fine-

tuning, the model can generate relevant outputs based on the

instructions provided in the prompt.

For example:

Text classification: The model can categorize text without

specific training, simply by understanding the prompt's context.

Translation or summarization: LLMs can translate or summarize

text using provided instructions, even without task-specific fine-

tuning.

This shows the LLMs' ability to generalize across tasks, making them

versatile for various applications.

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Q40. How does Adaptive Softmax speed up large language models?

Adaptive Softmax accelerates LLMs by categorizing words into

frequency groups, allowing for fewer computations for infrequent

words. This approach lowers overall computational costs while

preserving accuracy, making it effective for efficiently managing

large vocabularies.

Bhavishya Pandit

Q41. What is the vanishing gradient problem, and how does the

Transformer architecture address it?

Ans -

The vanishing gradient problem occurs when gradients diminish

during backpropagation, preventing deep networks from learning

effectively, especially in models like RNNs that handle long

sequences.

Transformers address this by using:

Self-Attention Mechanism: Captures relationships between all

tokens in a sequence simultaneously, avoiding sequential

dependencies and preventing gradient shrinkage over time.

Residual Connections: Skip connections between layers allow

gradients to flow directly, ensuring they remain strong

throughout backpropagation.

Layer Normalization: Normalizes inputs within each layer,

stabilizing gradient updates and preventing vanishing or

exploding gradients.

These innovations enable deep models to learn efficiently, even for

long sequences, overcoming the limitations of earlier architectures.

Bhavishya Pandit

Q42. Explain the concept of "few-shot learning" in LLMs and its

advantages.

Ans - Few-shot learning in LLMs is the ability of the model to

understand and tackle new tasks with just a few examples. This is

made possible by the model's extensive pre-training, which allows it

to generalize from limited data.

The main benefits of few-shot learning include:

Reduced Data Needs: It requires fewer examples to perform well,

minimizing the need for large, task-specific datasets.

Increased Flexibility: The model can easily adapt to various tasks

with minimal additional training.

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Cost Efficiency: With less need for extensive data and reduced

training times, it lowers the costs associated with data collection

and computational resources.

Interpretability: It can be challenging to understand and explain

how LLMs make their decisions due to their complex and often

opaque nature.

Data Privacy: Training on large datasets can raise concerns about

data privacy and security.

Cost: Developing, training, and deploying LLMs can be costly,

which may limit their use for smaller organizations.

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Q43. You're working on an LLM, and it starts generating offensive or

factually incorrect outputs. How would you diagnose and address

this issue?

Ans - If an LLM produces offensive or inaccurate outputs, I would first

analyze the patterns, check input prompts, and assess if the issue

stems from biases or gaps in the training data. I'd review the

preprocessing pipeline for errors or biases and examine the dataset

for imbalances.

Next, I’d evaluate the model’s architecture, hyperparameters, and

fine-tuning to identify any structural issues. Solutions could include

adversarial training, debiasing, data augmentation, or retraining

with a more balanced dataset.

Analyze Patterns Check

Hyperparameters

Review

Preprocessing

Adversarial

Training

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Q44. How is the encoder different from the decoder?

Ans -

In the transformer architecture used in large language models, the

encoder and decoder serve different purposes. The encoder processes

the input data and transforms it into a set of abstract

representations. The decoder then takes these representations and

generates the output, using both the information from the encoder

and the previously generated elements in the sequence. Essentially,

the encoder is responsible for understanding the input, while the

decoder focuses on producing the final output.

Bhavishya Pandit

Q45. What are the main differences between LLMs and traditional

statistical language models?

Ans -

Architecture: LLMs are based on transformers with self-

attention, which captures long-range dependencies, unlike

traditional models like N-grams or HMMs that struggle with this.

Scale: LLMs have billions of parameters and train on massive

datasets, enabling better generalization. Traditional models are

smaller and task-specific.

Training: LLMs undergo unsupervised pre-training and fine-

tuning. Traditional models rely on supervised learning with

labeled data for each task.

Input: LLMs handle variable-length inputs using advanced

tokenization like BPE, whereas traditional models often use

fixed-length inputs and simpler tokenization.

Context: LLMs generate contextual embeddings, adapting to

word meaning based on context. Traditional

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models use static embeddings.

Flexibility: LLMs can tackle multiple NLP tasks with little fine-

tuning, while traditional models are designed for specific tasks.

Resources: LLMs demand high computational power, requiring

GPUs or TPUs, whereas traditional models are more lightweight.

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Q46. What is a “context window”?

Ans - The "context window" in large language models (LLMs) is the

span of text—measured in tokens or words—that the model can

process at any given moment when generating or interpreting

language. The importance of the context window lies in its influence

on the model's ability to produce coherent and contextually relevant

responses.

A larger context window means the model can incorporate more

surrounding information, which enhances its understanding and

ability to generate text, particularly in more complex or extended

interactions. However, increasing the context window also raises

computational demands, so there’s a trade-off between improved

performance and resource efficiency.

Context Window

0

∞

Tokens

Bhavishya Pandit

Q47. What is a hyperparameter?

Ans - A hyperparameter is a parameter that is set before the training

process begins and influences how the model is trained. It controls

aspects of the training process and is chosen by the developer or

researcher based on prior knowledge or experimentation. Common

examples of hyperparameters include the model's architecture, batch

size, regularization strength, and learning rate.

Some common examples

Train-test split ratio

Learning rate in optimization algorithms (e.g. gradient descent)

Choice of optimization algorithm (e.g., gradient descent,

stochastic gradient descent, or Adam optimizer)

Choice of activation function in a neural network (nn) layer (e.g.

Sigmoid, ReLU, Tanh)

Bhavishya Pandit

Q48. Can you explain the concept of attention mechanisms in

transformer models?

Ans - At a high level, attention allows the model to focus on different

parts of the input sequence when making predictions. Instead of

treating every word or token equally, the model learns to "attend" to

relevant words that contribute most to the current prediction,

regardless of their position in the sequence.

For example, in a sentence like "The dog chased the ball because it

was fast," the word "it" could refer to either the dog or the ball. The

attention mechanism helps the model figure out that "it" is likely

referring to "the ball" based on the context.

Bhavishya PanditBhavishya Pandit

Q49. What are Large Language Models?

Ans -

A Large Language Model (LLM) is an Al system trained on vast

amounts of text to understand, generate, and predict human-like

language. It learns patterns, context, and relationships in the data to

produce relevant, coherent responses.

LLMs can handle a wide range of tasks, from answering questions and

summarizing text to performing translations and even creative

writing. Their ability to generalize across different language tasks

comes from training train on diverse datasets, allowing them to

generate contextually appropriate and meaningful content based on

the input they receive.

Bhavishya Pandit

Q50. What are some common challenges associated with using

LLMs?

Ans - Using LLMs presents several common challenges:

Computational Resources: They require substantial computing

power and memory, making both training and deployment

demanding.

Bias and Fairness: LLMs might learn and reproduce biases from

their training data, potentially leading to biased or unfair

outputs.

Interpretability: It can be challenging to understand and explain

how LLMs make their decisions due to their complex and often

opaque nature.

Data Privacy: Training on large datasets can raise concerns about

data privacy and security.

Cost: Developing, training, and deploying LLMs can be costly,

which may limit their use for smaller organizations.

Bhavishya Pandit

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